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. 2025 Aug 25;11:100293. doi: 10.1016/j.fochms.2025.100293

Integrated multi-omics reveals potential regulatory mechanisms of meat quality

Jiajia Wang a,1, Jiayu Liao a, Kaisen Zhao b, Na Shen a, Yulin Xu a, Jie Wang a, Xianbo Jia a, Wenqiang Sun a, Songjia Lai a,⁎
PMCID: PMC12433507  PMID: 40955366

Abstract

We hypothesized that the differences in meat quality stem from breed-specific regulation of muscle metabolic pathways. To explore the molecular mechanisms underlying meat quality differences between Liangshan cattle and Simmental crossbred cattle, we conducted metabolomic and transcriptomic analyses of the longissimus dorsi. Metabolomics revealed that Liangshan cattle exhibited higher l-carnitine levels, which may contribute to enhanced energy metabolism and improved meat quality, as well as a more favorable fatty acid composition. Integrated transcriptomic and metabolomic analysis suggested potential regulatory mechanisms: FASN may enhance fat deposition and tenderness by promoting butanoyl-CoA biosynthesis. Additionally, ALDOC, PFKL, PGAM1, and SDS may modulate citrulline metabolic flux, thereby influencing protein synthesis and promoting lipogenesis. These findings support our hypothesis by clarifying how coordinated gene–metabolite interactions influence meat quality. A potential regulatory model for the genetic-metabolic interaction of beef quality was established, providing a candidate framework for molecular breeding targeting beef quality traits.

Keywords: Cattle, Beef, Longissimus dorsi, Metabolomics, Transcriptomics

Highlights

Liangshan cattle, a native Chinese breed, exhibit robust adaptability, disease resistance, and superior meat quality with high protein/calcium content, but suffer from slow growth and low carcass yield. Simmental crossbred cattle are dominant in China's beef industry. Our study used metabolomics and transcriptomics to analyze the longissimus dorsi muscle of Simmental × Liangshan crossbred cattle versus purebred Liangshan cattle and identified key regulatory genes influencing meat quality. Key findings include:

  • •

    Liangshan cattle have better meat quality than Simmental crossbred cattle.

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    FASN promoted butanoyl-CoA biosynthesis, while SLMAP inhibited this pathway.

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    Joint multi-omics analysis revealed lower citrulline in Liangshan cattle longissimus dorsi.

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    ALDOC, PFKL, PGAM1, and SDS were upregulated and may modulate citrulline flux.

These findings provide actionable insights into molecular breeding strategies for improving beef quality.

1. Introduction

Metabolomics can directly reflect the outcome of complex networks of biochemical reactions, thus providing insights into multiple aspects of cellular physiology (Liu & Locasale, 2017). Transcriptome sequencing can identify genotypes related to phenotypes, thereby linking biological phenotypes with molecular mechanisms. For instance, RNA-seq technology was used to screen fatty acid transporter candidate genes such as CD36, SLC27A1, and FABP3, whose differential expression may affect fatty acid composition (Zhang et al., 2022). The integration of multi-omics data is now increasingly employed to decipher the genetic regulation of complex traits (Novais et al., 2022).

Beef quality varies considerably across cattle breeds, influencing palatability and consumer acceptance. These differences are associated with breed-specific variations in the expression of genes, metabolites, and metabolic pathways (Chen et al., 2022). Integrated transcriptomic and metabolomic analyses have identified several genes regulating intramuscular fat (IMF) deposition, including RapGEF1, ITGB1, HOX gene cluster, and CCDC80. (Yu, Wang, et al., 2023; Yu et al., 2024; Yu, Tian, et al., 2023). In addition, unsaturated fatty acid (UFA) metabolism plays a role in IMF accumulation, mediated by metabolites such as EA and genes including ACOX3, HACD2, and SCD5 (Yu, Wang, et al., 2023). Other regulatory factors include INSIG2, which may influence adipose tissue development and lipid metabolism (Grzes et al., 2016), and PGK1 and PKM2, which are involved in postmortem muscle energy metabolism—affecting the tricarboxylic acid (TCA) cycle, oxidative phosphorylation, and muscle contraction (Huang et al., 2024). Furthermore, while elevated GLI1 expression has been linked to increased muscle formation and reduced tenderness, higher alanine content appears to enhance sensory traits (Sun et al., 2022). More recently, PPM1J has been implicated in meat quality regulation through protein dephosphorylation, influencing glycerophospholipid composition (Ma, Yang, et al., 2024). Finally, several metabolites—including anserine, C16:0, C16:1, and C18:1n9c—have emerged as promising biomarkers for superior meat quality (Duan et al., 2023).

Liangshan cattle, an indigenous breed from southwestern China with over 2000 years of breeding history, are valued for their robustness, adaptability to harsh conditions, and superior meat quality, including high calcium/protein levels, a balanced amino acid profile, and desirable flavor (Jia et al., 2023; Yan et al., 2018; Yan et al., 2024). In comparison, Simmental crossbred cattle, widely used in Chinese beef production, exhibit high growth rates and meat yield while retaining environmental adaptability, making them a practical reference in genetic studies.

In-depth analysis of the differences in the longissimus dorsi of Liangshan and Simmental crossbred cattle is of great significance for revealing the formation mechanism of beef quality, optimizing beef cattle breeding strategies, and meeting the diversified market needs. However, there are relatively few reports on the difference in meat quality between Liangshan cattle and Simmental crossbred cattle. Therefore, we assume that the differences in gene expression and metabolite expression among different breeds lead to the variations in their meat quality. The purpose of the study was to employ metabolomics and transcriptomics analysis to investigate differences in metabolite content and gene expression in the longissimus dorsi of Liangshan cattle and Simmental crossbred cattle. The research aimed to identify key genes associated with meat quality regulation, thereby providing new insights and a theoretical foundation for beef breeding and high-quality beef production.

2. Materials and methods

All procedures were conducted in accordance with Chinese animal testing laws, approved by the Experimental Animal Management Committee of Sichuan Agricultural University (Agreement number: DKY-B2019302083), and were authorized under the project license.

2.1. Experimental design and sampling

The beef cattle selected for the experiment were Liangshan cattle and Simmental × Liangshan crossbred cattle from Sichuan Junhao Agricultural Technology Co., LTD., which were raised under the same growing conditions. Three Simmental crossbred and three Liangshan cattle were selected for slaughter using random numbers generated with the RAND function in Microsoft Excel, which matched the animals' unique identification numbers. Simmental crossbred cattle were labeled as A1-A3, and Liangshan cattle were labeled as B1-B3.

Cattle were fasted for 24 h with free access to water before slaughter and were electrically stunned with a stunner for 5 s, bled, peeled, eviscerated, and split down the midline by Sichuan Junhao Agricultural Technology Co., LTD. Muscle tissues were collected from three biological replicates (individual animals) per group. All subsequent omics analyses were performed on these individual biological samples. For each sample, multi-point sampling of the longissimus dorsi was performed. The collected samples were transferred to 2 mL cryotubes, snap-frozen in liquid nitrogen, and immediately stored in an ultra-low temperature freezer at −80 °C. Subsequently, all samples were transported on dry ice to Biomarker Technologies Co., Ltd. (Beijing, China) for metabolite and RNA extraction, detection, and analysis.

2.2. Untargeted metabolomics extraction: LC–MS/MS analysis

The untargeted metabolomics workflow comprises 3 phases: metabolite extraction, instrumental analysis, and data processing. Metabolite extraction involves ultrasonic-assisted grinding with extraction solvent and magnetic beads, followed by static centrifugation. The supernatant undergoes vacuum drying, redissolution in solvent, and instrumental analysis. The LC/MS system for metabolomics analysis comprises Waters UPLC Acquity I-Class PLUS (Waters Corporation, Milford, USA) ultra-high performance liquid tandem Waters UPLC Xevo G2-XS QTOF (Waters Corporation, Milford, USA) high resolution mass spectrometer. The column used is purchased from Acquity UPLC HSS T3 1.8 μm 2.1*100 mm (Waters Corporation, Milford, USA). Positive ion mode: mobile phase A: 0.1 % formic acid (TCI, Tokyo, Japan) aqueous solution; mobile phase B: 0.1 % formic acid acetonitrile (Merck Chemical Technology Co., LTD., Shanghai, China). Negative ion mode: mobile phase A: 0.1 % formic acid aqueous solution; mobile phase B: 0.1 % formic acid acetonitrile.

The process of LC-MS/MS analysis is as follows: Waters UPLC Xevo G2-XS QTOF high resolution mass spectrometer can collect primary and secondary mass spectrometry data in MSe mode under the control of the acquisition software (MassLynx V4.2, Waters Corporation, Milford, USA). In each data acquisition cycle, dual-channel data acquisition can be performed on both low collision energy and high collision energy at the same time. The low collision energy is off, the high collision energy range is 10–40 V, and the scanning frequency is 0.2 s for a mass spectrum. The parameters of the ESI ion source are as follows: Capillary voltage: 2500 V (positive ion mode) or − 2000 V (negative ion mode); cone voltage: 30 V; ion source temperature: 100 °C; desolvent gas temperature: 500 °C; backflush gas flow rate: 50 L/h; Desolventizing gas flow rate: 800 L/h.

Raw mass spectrometry data from MassLynx V4.2 were processed in Progenesis QI for peak alignment and metabolite identification. The precursor and fragment ions had mass deviations of ≤100 ppm and < 50 ppm, respectively. Following the qualitative and quantitative metabolite analyses, data quality assessment, annotation, differential expression analysis, and functional enrichment analyses were carried out. Additionally, extensive data exploration was conducted on the BMKCloud platform (https://www.biocloud.net; Biomarker Technologies Co., Ltd., Beijing, China). Differentially abundant metabolites (DAMs) were defined by thresholds of fold change ≥2, VIP ≥ 1, and P < 0.05.

2.3. Targeted metabolic fatty acid extraction: GC–MS analysis

Samples were placed in 2 mL Eppendorf tubes (EP tubes) and extracted with 500 μL of extraction solution (isopropanol/n-hexane (Shanghai Anpu Experimental Technology Co., LTD., Shanghai, China), 2:3 v/v, containing 0.2 mg/L of internal standard), followed by vortex mixing for 30 s. The mixture was homogenized in a ball mill at 40 Hz for 4 min and then subjected to ultrasound treatment for 5 min in an ice-water bath. After centrifugation at 12,000 rpm (r = 85 mm) for 15 min at 4 °C, the supernatant was transferred to fresh 1.5 mL EP tubes. An additional 500 μL of extraction solution was added to the residue, vortex-mixed for 30 s, and the homogenization and centrifugation steps were repeated. The supernatants from both centrifugation steps were combined and vortexed for 10 s. A volume of 800 μL of the combined supernatant was transferred to fresh 2 mL EP tubes and dried under a nitrogen stream. Subsequently, 500 μL of methanol (Shanghai Anpu Experimental Technology Co., LTD., Shanghai, China)/trimethylsilyl diazomethane (Shanghai Maclean Biochemical Technology Co., LTD., Shanghai, China) solution (1:2, v/v) was added for derivatization, and the mixture was incubated at room temperature for 30 min. After drying under nitrogen, 160 μL of n-hexane was added to redissolve the sample, followed by centrifugation at 12,000 rpm (r = 85 mm) for 1 min. The final supernatant was transferred to a GC–MS vial for analysis.

GC–MS analysis was performed using an Agilent 7890B (Agilent Technologies, Santa Clara, USA) gas chromatograph coupled with an Agilent 5977B (90 m × 250 μm × 0.25 μm) (Agilent Technologies, Santa Clara, USA) mass spectrometer equipped with a DB-FastFAME (Agilent Technologies, Santa Clara, USA) capillary column. A 1 μL aliquot was injected in split mode (5:1 ratio). Helium served as the carrier gas with a front inlet purge flow of 3 mL/min and a constant column pressure of 317 kPa. The temperature program was set as follows: initial hold at 50 °C for 1 min, ramped to 200 °C at 50 °C/min (15 min hold), then increased to 210 °C at 2 °C/min (1 min hold), followed by a final ramp to 230 °C at 10 °C/min (15 min hold). The injection port, transfer line, quadrupole, and ion source temperatures were maintained at 240 °C, 240 °C, 150 °C, and 230 °C, respectively. Electron impact ionization was conducted at 70 eV. Mass spectra were acquired in Scan/SIM mode with an m/z range of 33–400 after a 7 min solvent delay.

2.4. Targeted metabolic amino acid extraction: UHPLC-MS/MS analysis

2.4.1. Metabolite extraction

Samples were weighed into 2 mL centrifuge tubes containing steel beads. To precipitate proteins and extract metabolites, 1 mL of ice-cold methanol/acetonitrile (Merck Chemical Technology Co., Ltd., Shanghai, China)/water (2:2:1, v/v/v) solution, spiked with isotopically labeled internal standards of all target amino acids, was added to the samples. The mixtures were then vigorously vortexed for 30 s to ensure complete mixing and precipitation. Homogenization was performed at 45 Hz for 4 min using a bead mill, followed by 5 min sonication in an ice-water bath. This homogenization-sonication cycle was repeated three times. Subsequently, samples were incubated at −20 °C for 1 h and centrifuged at 12,000 rpm (r = 70 mm) for 15 min at 4 °C. A 200 μL aliquot of supernatant was filtered through 0.22 μm membranes before UHPLC-MS/MS analysis.

2.4.2. Standard solution preparation

Standard stock solutions (1000 ng/mL) were accurately prepared in volumetric flasks. Aliquots of these stocks were combined in a 10 mL flask to generate a mixed working standard solution. Calibration standards were subsequently prepared by serial dilution of this mixture, spiked with isotopically labeled internal standards at concentrations matching the sample matrix.

2.4.3. UHPLC-MRM-MS analysis

Chromatographic separation was performed using a Waters ACQUITY I-Class UHPLC (Waters Corporation, Milford, USA) system equipped with an ACQUITY UPLC BEH Amide column (150 mm × 2.1 mm, 1.7 μm; Waters Corporation, Milford, USA). The mobile phase consisted of (A) 0.2 % (v/v) formic acid (Thermo Fisher Scientific Co., Ltd., Shanghai, China) in water and (B) 0.2 % (v/v) formic acid in acetonitrile (Merck Chemical Technology Co., LTD., Shanghai, China). The column temperature was maintained at 35 °C, the auto-sampler at 10 °C, and a 1 μL injection volume was employed for all analyses. Mass spectrometric detection utilized a SCIEX 6500 QTRAP+ (Shanghai AB SCIEX Analytical Instrument Trading Co., Ltd., Shanghai, China) triple quadrupole system with an IonDrive Turbo V electrospray ionization (ESI) source operating under optimized parameters: curtain gas 241 kPa, ion spray voltage ±5500 V (positive/negative modes), source temperature 550 °C, nebulizer gas (Gas 1) 345 kPa, and heater gas (Gas 2) 379 kPa.

Multiple reaction monitoring (MRM) transitions were optimized via flow injection analysis of individual analyte standards (100 ng/mL). For each analyte, collision energy was systematically optimized by evaluating 3–5 precursor-product ion transitions. The transition exhibiting optimal sensitivity and selectivity was selected as the quantifier ion, while secondary transitions served as qualifiers for identity confirmation. Instrument control, data acquisition, and processing were conducted using SCIEX Analyst Software (v1.7.2) and Sciex OS (v2.0.1).

2.5. RNA-sequencing

Total RNA extraction, RNA integrity assessment, library construction, and RNA-seq were performed by Biomarker Technologies Co., Ltd. (Beijing, China). Specifically, RNA was first extracted from longissimus dorsi tissue using the TRIzol Reagent (Life Technologies, California, USA). RNA concentration and purity were measured using NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, USA).

Following successful quality control, the library preparation process was conducted through the following steps. A total amount of 1 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using the Hieff NGS Ultima Dual-mode mRNA Library Prep Kit for Illumina (Yeasen Biotechnology (Shanghai) Co., Ltd.), following the manufacturer's recommendations. Index codes were added to attribute sequences for each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. First-strand cDNA was synthesized, and second-strand cDNA synthesis was subsequently performed. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3′ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure was ligated to prepare for hybridization. The library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA). Then 3 μL USER Enzyme (NEB, Ipswich, USA) was used with size-selected, adaptor-ligated cDNA at 37 °C for 15 min, followed by 5 min at 95 °C before PCR. Then, PCR was performed using Phusion High-Fidelity DNA polymerase, Universal PCR primers, and an Index (X) Primer. At last, PCR products were purified (AMPure XP system), and library quality was assessed on the Agilent Bioanalyzer 2100 system.

The clean reads were aligned to the cattle reference genome Bos_taurus.ARS_UCD_1.3. genome. fa using HISAT2 (v2.2.1) software. Based on count values, differentially expressed genes (DEGs) were identified using DESeq2 for datasets with biological replicates or EBSeq for those without replicates, with screening criteria set at Fold Change ≥2 and FDR < 0.05. StringTie (v2.2.1) was used to standardize through the maximum flow algorithm using Fragments Per Kilobase of transcript per Million fragments mapped (FPKM, FPKM = cDNAFragments / MappedFragments (Millions) * TranscriptLength (kb)) as an indicator of gene expression levels. Gene function was annotated based on the following databases: Nr (NCBI non-redundant protein sequences); Pfam (Protein family); KOG/COG (Clusters of Orthologous Groups of proteins); Swiss-Prot (A manually annotated and reviewed protein sequence database); KO (KEGG Ortholog database); GO (Gene Ontology). Functional enrichment analyses were subsequently performed using R packages clusterProfiler and topGO, including Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the identified DEGs.

3. Results

3.1. Differential abundance of metabolites: l-carnitine and 12,13-DHOME

A total of 15,294 peaks were detected, of which 1769 metabolites were noted. In the principal component analysis, PC1 and PC2 cumulatively explained 54.36 % of the variance between groups (Fig. 1A). Spearman's correlation coefficient (r) was close to 1.0, indicating the reliability of the DAMs analysis method (Fig. 1B). In the orthogonal partial least squares discriminant analysis (OPLS-DA), the Q2 value of the paired comparison between A-B and B-B was 0.457 (Fig. 1C). Further substitution tests found that the slope of the regression line of Q2Y was positive, indicating that the constructed model was more appropriate (Fig. 1D). Metabolites were annotated according to the KEGG database (Fig. 1E). Then, DAMs were screened according to the following criteria: |log2(FC) | ≥ 1 and VIP ≥ 1.

Fig. 1.

Fig. 1

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Basal analysis of metabolites in the samples. (A) PCA score plot of untargeted metabolomics. (B) Correlation analysis between sample metabolomes. (C) Orthogonal projections to latent structures- discriminant analysis (OPLS-DA) score plot of metabolomics data. The x-axis represents the difference between groups, and the y-axis represents the difference within groups. (D) Permutation plot test of OPLS-DA model. To check the reliability of the OPLS-DA model, permutation tests need to be conducted. Randomly shuffle (permutation) the grouping of the samples, conduct OPLS-DA modeling according to the permutation grouping and calculate its R2Y and Q2Y. Repeat this process many times, and plot the results of multiple modeling as a scatter plot. (E) Metabolites were annotated in KEGG database.

In groups A and B, a total of 1769 metabolites were detected, including 50 differential metabolites (19 upregulated and 31 downregulated in Liangshan cattle) (Fig. 2A, Table S1). A cluster analysis was performed on the 50 DAMs selected (Fig. 2B), from which the samples could be grouped into two classes, suggesting that these aggregated metabolites might have similar functional annotations or be involved in the same metabolic pathway, and the expression differences between the samples were evident. KEGG enrichment analysis at the metabolic group level showed that 22 pathways were significantly enriched (P < 0.05). They were mainly enriched in polyketide sugar unit biosynthesis, linoleic acid metabolism, primary bile acid biosynthesis, lysine degradation, and the AMPK signaling pathway (Fig. 2C and Tables S2, 3). Among these metabolites, l-carnitine hydrochloride was most significantly upregulated in Liangshan cattle, and 12, 13-DHOME was most significantly downregulated in Liangshan cattle.

Fig. 2.

Fig. 2

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Basic data analysis of DAMs. (A) Volcano plot of DAMs between A-B and B-B. The red dots represent differential metabolites that were significantly up-regulated in the experimental group, the blue dots represent differential metabolites that were significantly down-regulated, and the grey dots represent differential metabolites that were not significant. (B) Heatmap of DAMs between A-B and B-B. (C) Bubble plot of KEGG pathway enrichment analysis for DAMs between A-B and B-B. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.2. Differential abundance of fatty acids: Liangshan cattle contain more long-chain fatty acids

The fatty acids in 6 bovine longissimus dorsi samples were detected by the GC–MS method, and 49 fatty acids were extracted from the samples. To reduce the impact of detection system errors on the analysis results, missing values in the raw data were preprocessed. First, missing value filtering was performed: for individual fatty acids, only peak area data with a missing value proportion of not more than 50 % in either any single sample group or across all sample groups combined were retained. Subsequently, missing value imputation was performed: missing values in the raw data were recoded by multiplying the minimum value of each fatty acid across all samples by a randomly generated number between 0.1 and 0.5. Ultimately, 26 fatty acids were preserved. In the principal component analysis, PC1 and PC2 cumulatively explained 80.5 % of the variance among the groups, and the results of PCA score plots showed that all samples were within 95 % confidence intervals (Fig. 3A). In the orthogonal partial least squares discriminant analysis (OPLS-DA), the Q2 value of the paired comparison between A-B and B-B was 0.786 (Fig. 3B). The regression line slope of Q2 in the dot plot of the replacement test results of the OPLS-DA model was positive, indicating that the constructed model was more appropriate (Fig. 3C).

Fig. 3.

Fig. 3

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Basic analysis of fatty acids data. (A) PCA score plot of fatty acids. (B) OPLS-DA score plot of fatty acids data. (C) Permutation plot test of OPLS-DA model.

The 26 retained fatty acids were all long-chain fatty acids, of which 17 were upregulated and 9 were downregulated in Liangshan cattle, accounting for 65.38 % and 34.62 % of the total differential abundance fatty acids (DAFAs), respectively (Fig. 4A and Table S4). Compared with Simmental crossbred cattle, the longissimus dorsi of Liangshan cattle contains more long-chain fatty acids. The distribution of each metabolite across different groups could be seen from the Z-score plot (Fig. 4B). Cluster analysis was performed on 26 screened fatty acids (Fig. 4C), and the result showed that the samples could be classified into two categories, suggesting that these clustered fatty acids might share similar functional annotations or belong to the same metabolic pathway, with obvious abundance differences between samples. After annotating the KEGG metabolic pathway, fatty acids were classified according to the results of KEGG pathway annotation, and the KEGG classification diagram was obtained (Fig. 4D). KEGG enrichment analysis showed that 26 pathways were significantly enriched (P < 0.05). These pathways were mainly distributed in the biosynthesis of unsaturated fatty acids (UFAs), metabolic pathways, fatty acid biosynthesis, and linoleic acid metabolism (Fig. 4E and Table S5).

Fig. 4.

Fig. 4

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Basic data analysis of DAFAs. (A) Volcano plot of DAFAs between A-B and B-B. (B) Z-score plot for DAFAs, the horizontal coordinate represents the Z value, the vertical coordinate represents the metabolite, and the dots of different colors represent different groups of samples. (C) Heatmap of DAFAs between A-B and B-B. (D) KEGG Classification for DAMs. (E) KEGG Enrichment bubble for DAFAs between A-B and B-B.

3.3. Higher amino acid content in Liangshan cattle

The ion chromatograms (EICs) of the standard solution and sample extraction were shown in Fig. 5A and B, showing that all target compounds in the analytical method adopted in the project presented symmetric chromatographic peaks. Each target compound was well separated chromatographically. There was no significant difference in the retention time and chromatographic peak shape between the biological samples and the standard solution. Table S6 (Quantification Results) showed the quantitative parameters of the target compounds. The lowest detection limits (LLODs) of amino acid target compounds ranged from 0.25 to 10.00 ng/mL, and the LLOQs ranged from 0.5 to 20.00 ng/mL. The correlation coefficient (R) for all the target compounds was greater than 0.9976, indicating a good quantitative relationship between the chromatographic peak area and the concentration of the compounds, which could meet the requirements of targeted metabolomics analysis (Table S6: Calibration Curve). The recovery rate and relative standard deviation (RSD) of QC samples are shown in Table S6 Recovery and RSD, and the number of repeated injections of QC samples was six. As shown in the table, the average recoveries of all target compounds ranged from 83.84 % to 102.78 %, and the standard relative deviations were less than 4.88 %. The above data showed that the method could accurately and reliably detect the target metabolite content in the sample within the concentration range shown above.

Fig. 5.

Fig. 5

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Basic analysis of amino acids data. (A) EIC of standard solution. (B) EIC of samples solution. (C) PCA score plot of amino acids. (D) Heatmaps of DAAAs in A-B and B-B. (E) Volcano plot.

In the principal component analysis, PC1 and PC2 cumulatively explained 78.09 % of the variance between groups (Fig. 5C). See Fig. 5D for the overall sample clustering heat map. Differentially abundant amino acids (DAAAs) were screened according to the following criteria: |log2 (FC) | ≥ 1 and VIP ≥ 1. In groups A and B, although no significant difference in the 37 amino acids in the samples was detected, the total amino acid content in the longissimus dorsi of Liangshan cattle was higher than that of Simmental crossbred cattle (Fig. 5E and Table S7). L-carnosine, reduced glutathione, L-arginine, l-lysine, L-histidine, l-glutamine, L-leucine, L-phenylalanine, L-4-hydroxyproline, L-asparagine, L-threonine, L-isoleucine, L-valine, L-glutamic acid, beta-alanine, 3-methyl-L-histidine, L-methionine, l-serine, L-proline, glycine, L-tryptophan, and 1-methyl-L-histidine in Liangshan cattle are higher than in Simmental crossbred cattle. In Liangshan cattle, N-acetyl-L-methionine, D, L-o-tyrosine, hippuric acid, 4-aminobutyric acid, L-ornithine, L-citrulline, L-alanine, and L-aspartic acid content were lower than that of Simmental crossbred cattle.

3.4. Differential expression gene

Further exploration of molecular-level differences in the longissimus dorsi between Liangshan cattle and Simmental crossbred cattle was conducted. Transcriptome sequencing was employed to identify differentially expressed genes (DEGs) in the longissimus dorsi of Liangshan cattle compared with Simmental crossbred cattle. RNA-seq analysis of six samples generated 39.17 Gb of clean data, with each sample yielding at least 6.07 Gb of clean data. The Q30 base percentage exceeded 98.60 % (Table S8), and the mapping ratio of clean reads ranged between 96.96 % and 98.64 % (Table S9), confirming the reliability of the transcriptomic data. Compared to Simmental crossbred cattle, Liangshan cattle exhibited 465 DEGs, including 294 upregulated and 171 downregulated genes (Fig. 6A, Table S10).

Fig. 6.

Fig. 6

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Basal analysis of transcriptome. (A) Volcano plots of DEGs in A-B and B-B. (B) Heatmap of DEGs in A-B and B-B. (C) GO enrichment analysis of DEGs in A-B and B-B. (D) Bubble plot of KEGG pathway enrichment analysis of DEGs in A-B and B-B.

Cluster analysis was performed on 465 DEGs (Fig. 6B), and the result showed that the samples could be classified into two categories. Functional annotation and classification of the identified DEGs were performed using Gene Ontology (GO) analysis. A total of 42 enriched subcategories were detected, including 22 Biological Process (BP) terms, 4 Cellular Component (CC) terms, and 16 Molecular Function (MF) terms. Among these, 296 genes were associated with “cellular process” in BP, 334 genes with “cellular anatomical entity” in CC, and 257 genes with “binding” in MF (Fig. 6C, Table S11). KEGG enrichment analysis revealed that these DEGs were primarily involved in pathways such as complement and coagulation cascades, Fc gamma receptor-mediated phagocytosis, insulin signaling pathway, and histidine metabolism in the longissimus dorsi of Liangshan cattle compared with Simmental crossbred cattle (Fig. 6D).

3.5. Joint analysis of the transcriptome and metabolome

Organisms regulate their metabolic processes through gene expression control. To investigate metabolic disparities between two cattle breeds, we performed an integrated transcriptomic-metabolomic analysis. Using Weighted Gene Co-expression Network Analysis (WGCNA), genes and metabolites were clustered into co-expression modules through separate processing of transcriptomic and metabolomic datasets, followed by inter-module correlation evaluation. This approach identified 24 metabolite modules (ME) and 150 gene modules (GE) (Fig. 7A). The strongest correlation (r = 0.99, P < 0.01) was observed between the GElightgreen module (89 genes; e.g., PSMB4, FITM1, DPF3) and the MEdarkgreen module (32 metabolites; e.g., nona-4,7-dienedioylcarnitine, trans-tetradec-2-enoyl-CoA) (Tables S12, 13).

Fig. 7.

Fig. 7

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Joint Analysis of the Transcriptome and Metabolome. (A) Heatmap of the correlation between metabolite module and gene module. The gene module is indicated on the right, the bottom shows the metabolite module, and the left and top show the clustering dendrograms of genes and metabolites. The closer the absolute value is to 1, the higher the correlation. Red indicates a positive correlation, while green indicates a negative correlation, with darker colors indicating stronger correlations. Asterisks indicate a significant correlation between metabolites and genes; *P < 0.05, **P < 0.01, and ***P < 0.001. (B) Bubble plot of significant KEGG pathway enrichment for DEGs/DAMs. KEGG pathway mapping of DEGs and DAMs in fatty acid metabolism pathway (C) and biosynthesis of amino acids pathway (D). Squares denote gene products and circles represent metabolites. Red indicates upregulated genes/metabolites, while green indicates downregulated genes/metabolites. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

KEGG pathway analysis revealed significant enrichment of core biological processes impacting meat quality, including: fatty acid elongation, histidine metabolism, linoleic acid metabolism, butanoate metabolism, nicotinate/nicotinamide metabolism, and amino acid biosynthesis (Fig. 7B and Table S14). Notably, most enriched pathways were functionally linked to fatty acid and amino acid metabolism. In the fatty acid metabolism pathway (ko01212), Liangshan cattle exhibited significant upregulation of butanoyl-CoA and FASN, with concurrent downregulation of SLMAP compared to Simmental crossbreds (Fig. 7C). Within the biosynthesis of amino acids (ko01230), ALDOC, PFKL, PGAM1, and SDS were upregulated, whereas L-Citrulline showed reduced levels (Fig. 7D and Table S15).

4. Discussion

The quality of beef plays a significant role in consumer appeal, with key factors such as fatty acids, amino acids, and gene expression influencing both the texture and flavor of meat. These components not only affect tenderness and flavor but also contribute to the overall nutritional value and health benefits of beef. In this study, we uncovered how biochemical and genetic factors affect beef quality by examining differences in the longissimus dorsi muscle between Liangshan cattle and Simmental crossbred cattle.

4.1. Roles of DAMs in beef quality

l-Carnitine effectively enhances intramuscular fat content by promoting fatty acid transport and oxidation (Ferreira & McKenna, 2017), thereby improving meat flavor, tenderness, color, and muscle water-holding capacity. l-carnitine hydrochloride is a hydrochloride form of l-carnitine. 12, 13-DHOME h hurts the energy of neutrophils (Thompson & Hammock, 2007), and the increase of 12, 13-DHOME might mediate the occurrence of immunosuppression (Edwards et al., 2012). In Liangshan cattle, the upregulation of l-Carnitine hydrochloride implies an increase in l-Carnitine, suggesting that the meat quality might be better than that of Simmental crossbred cattle, showing better flavor characteristics. At the same time, the downward adjustment of 12, 13-DHOME in Liangshan cattle might mean that Liangshan cattle have stronger immunity compared with Simmental crossbred cattle. The discovery is consistent with the excellent adaptability and resistance shown by China's domestic cattle species, which further confirms the superiority of native cattle species, and also explains the reason why local people have raised Liangshan cattle for thousands of years.

4.2. Fatty acids and beef quality

Fatty acids are crucial determinants of beef quality, influencing both flavor and texture. Specifically, the composition of fatty acids in muscle and adipose tissue impacts the oxidative stability of muscle, as well as the firmness and oiliness of adipose tissue, which directly affects meat quality and flavor (Wood et al., 2007). Different types of fatty acids contribute distinct flavor characteristics to beef (Arshad et al., 2018). Compared with male yaks, the absolute contents of UFAs, monounsaturated fatty acids, and polyunsaturated fatty acids in the subcutaneous fat of female yaks were all higher (Xiong et al., 2022). Mottram and Edwards reported the relations among 14:1, 16:1, 18:0, 18:1, 18:2, 18:3 fatty acids and desired beef flavor (Mottram & Edwards, 1983). Notably, the contents of long-chain UFAs such as C16:0, C16:1, C18:1n9c, and others have been shown to be positively correlated with intramuscular fat and pH, which influence flavor (Yehui et al., 2022). Similarly, the features of meat like juiciness, flavor, firmness of the fat, and shelf life are affected by the fatty-acid composition (Wood et al., 2004). The main fatty acids that play a role in the flavor of beef are UFAs (Liu et al., 2020). And it is generally believed that a higher intake of UFAs is beneficial to body health and can significantly reduce the risk of cardiovascular disease (de Souza et al., 2015).

In our study, Liangshan cattle were found to have higher concentrations of long-chain UFAs in the longissimus dorsi compared to Simmental crossbred cattle. This suggests that beef from Liangshan cattle is likely to have better flavor and is potentially more beneficial for consumers' health.

4.3. Amino acids and their role in beef quality

Amino acids also play a vital role in determining the quality of beef. They are not only the fundamental building blocks of protein but also contribute to the generation of volatile and non-volatile flavor compounds (Bermúdez et al., 2014). It is well known that glutamic acid is one of the main sources of umami. The higher the content of glutamic acid, the more delicious the beef tastes. Lysine and glutamine are the two most abundant amino acids in beef, and they contribute to the formation of beef flavor (Yu et al., 2024). The contents of essential amino acids for infants, such as l-lysine, L-histidine, L-leucine, L-phenylalanine, L-isoleucine, L-valine, L-tryptophan, and L-arginine, in Liangshan cattle are higher than those in Simmental crossbred cattle.

Furthermore, although the types of amino acids found in the longissimus dorsi of both cattle breeds did not show significant differences, the concentration of specific amino acids such as glutamic acid, lysine, and glutamine were higher in Liangshan cattle. This suggests that the flavor and texture of meat from Liangshan cattle may be superior to that of Simmental crossbred cattle, further supporting the notion that local breeds offer distinct advantages in meat quality.

4.4. Gene expression and metabolic pathways

Beyond fatty acids and amino acids, gene expression plays a critical role in regulating the quality of meat. In particular, genes involved in intramuscular fat (IMF) regulation are central to determining beef quality.FASN is a typical lipogenic gene (Hausman et al., 2009) that plays a key role in the fatty acid synthesis pathway. Zappaterra et al. showed that the c.265 T > C SNP FASN polymorphism significantly changes the content of stearic, arachidonic, dihomo-γ-linolenic, and arachidonic fatty acids in the longissimus dorsi of Italian Large White pigs (Zappaterra et al., 2019). Compared to Simmental cattle, FASN was significantly upregulated in Pingliang Red Bull and Wagyu cross F1 generation (Wang et al., 2023).

Butyric acid produced by Clostridium butyricum can promote skeletal muscle development of lambs, resulting in reduced shear force, brighter red color, increased fat deposition, and improved tenderness of lambs (Dou et al., 2023). Butanoyl-CoA, which was involved in fatty acid metabolism, was a precursor of butyric acid, and its upregulation might lead to an increase in butyric acid in muscle, thereby improving the meat quality.

In our study, Liangshan cattle exhibit significantly higher FASN expression and butanoyl-CoA levels than Simmental crossbred cattle, suggesting that the meat quality of Liangshan cattle might be superior to that of Simmental crossbred cattle. Furthermore, we speculated that there is a functional association between FASN expression and the biosynthesis of butanoyl-CoA.

Citrulline is an amino acid that promotes protein synthesis and reduces fat. Some studies have shown that citrulline supplementation can enhance protein synthesis and muscle protein content (Goron et al., 2019; Jourdan et al., 2015; Osowska et al., 2006). It also promotes energy expenditure conducive to fat reduction by facilitating the β-oxidation of fatty acids (Joffin et al., 2015; Kudo et al., 2021). Research has demonstrated that ALDOC promotes lipid accumulation in intramuscular and intermuscular adipocytes by activating the AKT-mTORC1 signaling pathway (Ma, Wang, et al., 2024). PFKL enhances the β-oxidation of fatty acids (Meng et al., 2024), which might lead to reduced muscle fat accumulation. PGAM regulates meat tenderness, color, and pH (Przybylski et al., 2016). PGAM1, a subtype of the PGAM glycolytic enzyme (Song et al., 2018), might participate in the aforementioned regulatory processes. SDS is involved in serine metabolism, but current research offers no speculation on its impact on meat quality. In the study, within the amino acid biosynthesis pathway, citrulline content in the longissimus dorsi of Liangshan cattle was significantly lower than that in Simmental crossbred cattle, while the expression levels of genes including ALDOC, PFKL, PGAM1, and SDS were significantly upregulated. This indicates that the protein deposition in the muscle tissue of Liangshan cattle may be lower than that of Simmental crossbred cattle, but the fat content is higher than that of Simmental crossbred cattle, and these genes may be associated with citrulline synthesis or metabolic processes.

5. Conclusions

In summary, our findings demonstrate that Liangshan cattle exhibit superior meat quality compared to Simmental crossbred cattle. Mechanistically, FASN functions as a positive regulator of butanoyl-CoA biosynthesis. Notably, the coordinated upregulation of ALDOC, PFKL, PGAM1, and SDS significantly inhibits citrulline biosynthesis. Collectively, these findings delineate a dual regulatory network: (1) FASN-mediated control of butanoyl-CoA levels, and (2) ALDOC, PFKL, PGAM1, and SDS modulating citrulline metabolic flux.

These findings establish a potential regulatory model of genetic-metabolic interactions underlying beef quality, providing a candidate framework for molecular breeding targeting beef quality traits.

CRediT authorship contribution statement

Jiajia Wang: Writing – review & editing, Writing – original draft. Jiayu Liao: Methodology, Data curation. Kaisen Zhao: Writing – review & editing, Supervision. Na Shen: Writing – review & editing, Supervision. Yulin Xu: Writing – review & editing, Supervision. Jie Wang: Supervision, Project administration, Funding acquisition. Xianbo Jia: Writing – review & editing, Supervision. Wenqiang Sun: Supervision. Songjia Lai: Supervision, Resources.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This project was funded by the National Key Research and Development Program of China (No.2022YFD1601606).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.fochms.2025.100293.

Appendix A. Supplementary data

Table S1. DAMs. Table S2. Metabolites KEGG. Table S3. DAMs KEGG. Table S4. DAFAs. Table S5. Fatty Acids KEGG Enrichment data matrix. Table S6. Amino acids result. Table S7. DAAAs. Table S8. Sample GC Q.stat of Genes. Table S9. SeqData – RefGenome Comp of Genes. Table S10. DEGs. Table S11. GO annotation of DEGs. Table S12. Metabolites WGCNA module. Table S13. Genes WGCNA module. Table S14. KEGG enrichment of genes and metabolites. Table S15. DEGs and DEMs mapped to the KEGG pathway.

mmc1.zip (1.2MB, zip)

Data availability

Data will be made available on request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. DAMs. Table S2. Metabolites KEGG. Table S3. DAMs KEGG. Table S4. DAFAs. Table S5. Fatty Acids KEGG Enrichment data matrix. Table S6. Amino acids result. Table S7. DAAAs. Table S8. Sample GC Q.stat of Genes. Table S9. SeqData – RefGenome Comp of Genes. Table S10. DEGs. Table S11. GO annotation of DEGs. Table S12. Metabolites WGCNA module. Table S13. Genes WGCNA module. Table S14. KEGG enrichment of genes and metabolites. Table S15. DEGs and DEMs mapped to the KEGG pathway.

mmc1.zip (1.2MB, zip)

Data Availability Statement

Data will be made available on request.

Articles from Food Chemistry: Molecular Sciences are provided here courtesy of Elsevier

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